Drug Response Research Articles

Identifying subtypes and developing prognostic models based on N6-methyladenosine and immune microenvironment related genes in breast cancer

Breast cancer (BC) is the most prevalent cancer in women globally. The tumor microenvironment (TME), comprising epithelial tumor cells and stromal elements, is vital for breast tumor development. N6-methyladenosine (m6A) modification plays a key role in RNA metabolism, influencing its various aspects such as stability and translation. There is a notable link between m6A methylation and immune cells in the TME, although this relationship is complex and not fully deciphered. In this research, BC expression and clinicopathological data from TCGA were scrutinized to assess expression profiles, mutations, and CNVs of 31 m6A genes and immune microenvironment-related genes, examining their correlations, functions, and prognostic impacts. Lasso and Cox regression identified prognostic genes for constructing a nomogram. Single-cell analyses mapped the distribution and patterns of these genes in BC cell development. We investigated associations between gene-derived risk scores and factors like immune infiltration, TME, checkpoints, TMB, CSC indices, and drug response. As a complement to computational analyses, in vitro experiments were conducted to confirm these expression patterns. We included 31 m6A regulatory genes and discovered a correlation between these genes and the extent of immune cell infiltration. Subsequently, a 7-gene risk score was generated, encompassing HSPA2, TAP1, ULBP2, CXCL1, RBP1, STC2, and FLT3. It was observed that the low-risk group exhibited better overall survival (OS) in BC, with higher immune scores but lower tumor mutational burden (TMB) and cancer stem cell (CSC) indices, as well as lower IC50 values for commonly used drugs. To enhance clinical applicability, age and stage were incorporated into the risk score, and a more comprehensive nomogram was constructed to predict OS. This nomogram was validated and demonstrated good predictive performance, with area under the curve (AUC) values for 1-year, 3-year, and 5-year OS being 0.848, 0.807, and 0.759, respectively. Our findings highlight the profound impact of prognostic-related genes on BC immune response and prognostic outcomes, suggesting that modulation of the m6A-immune pathway could offer new avenues for personalized BC treatment and potentially improve clinical outcomes.

Targeting AKR1B10 by drug repurposing with epalrestat overcomes chemoresistance in non-small cell lung cancer patient-derived tumor organoids.

Systemic treatments given to non-small cell lung cancer (NSCLC) patients are often ineffective due to drug resistance. In the present study, we investigated patient-derived tumor organoids (PDTOs) and matched tumor tissues from surgically treated NSCLC patients to identify drug repurposing targets to overcome resistance towards standard-of-care platinum-based doublet chemotherapy. PDTOs were established from ten prospectively enrolled non-metastatic NSCLC patients from resected tumors. PDTOs were compared with matched tumor tissues by histopathology/immunohistochemistry, whole exome and transcriptome sequencing. PDTO growths and drug responses were determined by measuring 3D tumoroid volumes, cell viability, and proliferation/apoptosis. Differential gene expression analysis identified drug-repurposing targets. Validations were performed with internal/external NSCLC patient data sets. NSCLC cell lines were used for aldo-keto reductase 1B10 (AKR1B10) knockdown studies and xenograft models to determine the intratumoral bioavailability of epalrestat. PDTOs retained histomorphology and pathological biomarker expression, mutational/transcriptomic signatures, and cellular heterogeneity of the matched tumor tissues. Five (50%) PDTOs were chemoresistant towards carboplatin/paclitaxel. Chemoresistant PDTOs and matched tumor tissues demonstrated overexpression of AKR1B10. Epalrestat, an orally available AKR1B10 inhibitor in clinical use for diabetic polyneuropathy, was repurposed to overcome chemoresistance of PDTOs. In vivo efficacy of epalrestat to overcome drug resistance corresponded to intratumoral epalrestat levels. PDTOs are efficient preclinical models recapitulating the tumor characteristics and are suitable for drug testing. AKR1B10 can be targeted by repurposing epalrestat to overcome chemoresistance in NSCLC. Epalrestat has the potential to advance to clinical trials in drug-resistant NSCLC patients due to favorable toxicity, pharmacological profile, and bioavailability.

The synergy of artificial intelligence and personalized medicine for the enhanced diagnosis, treatment, and prevention of disease.

The completion of the Human Genome Project in 2003 marked the beginning of a transformative era in medicine. This milestone laid the foundation for personalized medicine, an innovative approach that customizes healthcare treatments. Central to the advancement of personalized medicine is the understanding of genetic variations and their impact on drug responses. The integration of artificial intelligence (AI) into drug response trials has been pivotal in this domain. These technologies excel in handling large-scale genomic datasets and patient histories, significantly improving diagnostic accuracy, disease prediction and drug discovery. They are particularly effective in addressing complex diseases such as cancer and genetic disorders. Furthermore, the advent of wearable technology, when combined with AI, propels personalized medicine forward by offering real-time health monitoring, which is crucial for early disease detection and management. The integration of AI into personalized medicine represents a significant advancement in healthcare, promising more accurate diagnoses, effective treatment plans and innovative drug discoveries. As technology continues to evolve, the role of AI in enhancing personalized medicine and transforming the healthcare landscape is expected to grow exponentially. This synergy between AI and healthcare holds great promise for the future, potentially revolutionizing the way healthcare is delivered and experienced.

Photoactive metabolite mediated photodynamic therapy of Rhabdomyosarcoma cell lines using medicinal plants and Doxorubicin co-treatments

BackgroundMedicinal plant-mediated combinational therapies have gained importance globally due to minimal side effects and enhanced treatment outcomes compared to single-drug modalities. We aimed to analyze the cytotoxic potential of each conventional treatment i.e., photodynamic therapy (PDT), chemotherapy (doxorubicin hydrochloride; Dox-HCl) with or without various concentrations of medicinal plant extracts (PE) on soft tissue cancer Rhabdomyosarcoma (RD) cell line.MethodsThe Rhabdomyosarcoma (RD) cell line was cultured and treated with Photosensitizer (Photosense (AlPc4)), Chemo (Dox-HCl), and their combinations with different concentrations of each plant extract i.e., Thuja occidentalis, Moringa oleifera, Solanum surattense. For the source of illumination, a Diode laser (λ = 630 nm ± 1 nm, Pmax = 1.5 mW) was used. Photosensitizer uptake time (∼ 45 min) was optimized through spectrophotometric measurements (absorption spectroscopy). Drug response of each treatment arm was assessed post 24 h of administration using 3-(4, 5-dimethyl-2-thiazolyl)-2, 5- 5-diphenyl-2 H- tetrazolium bromide (MTT) assay.ResultsPE-mediated Chemo-Photodynamic therapy (PDT) exhibited synergistic effects (CI < 1). Moreover, Rhabdomyosarcoma culture pretreated with various plant extracts for 24 h exhibited significant inhibition of cell viability however most effective outcomes were shown by low and high doses of Moringa oleifera compared to other plant extracts. Post low doses treated culture with all plant extracts followed by PDT came up with more effectiveness when compared to all di-therapy treatments.ConclusionThe general outcome of this work shows that the ethanolic plant extracts (higher doses) promote the death of cancerous cells in a dose-dependent way and combining Dox-HCl and photo-mediated photodynamic therapy can yield better therapeutic outcomes.

Building a translational cancer dependency map for The Cancer Genome Atlas.

Cancer dependency maps have accelerated the discovery of tumor vulnerabilities that can be exploited as drug targets when translatable to patients. The Cancer Genome Atlas (TCGA) is a compendium of 'maps' detailing the genetic, epigenetic and molecular changes that occur during the pathogenesis of cancer, yet it lacks a dependency map to translate gene essentiality in patient tumors. Here, we used machine learning to build translational dependency maps for patient tumors, which identified tumor vulnerabilities that predict drug responses and disease outcomes. A similar approach was used to map gene tolerability in healthy tissues to prioritize tumor vulnerabilities with the best therapeutic windows. A subset of patient-translatable synthetic lethalities were experimentally tested, including PAPSS1/PAPSS12 and CNOT7/CNOT78, which were validated in vitro and in vivo. Notably, PAPSS1 synthetic lethality was driven by collateral deletion of PAPSS2 with PTEN and was correlated with patient survival. Finally, the translational dependency map is provided as a web-based application for exploring tumor vulnerabilities.

The Long Noncoding RNA PANDAR as a Prognostic and Diagnostic Biomarker in Panceratic Cancer ‏‏.

Pancreatic cancer (PC) is a lethal complication in the world, affecting around half a million individuals each year. The treatment of PC is relatively difficult due to the difficulty in making an early diagnosis. Most PC patients are confronted with locally metastatic or advanced diseases in the asymptomatic phase, and about 80% have late diagnosis with metastasis. Recently, long noncoding RNAs (lncRNAs) have drawn attention as a novel biological regulation layer. They take part in the regulation of mRNA and can be used as a prognostic factor or drug target. Based on their functions as regulators of PC initiation and progress, the lncRNAs can be categorized as tumor suppressors or oncogenic. They can be considered as a target for finding new biomarkers for prognosis, diagnosis, monitoring, and treating drug response in PC. Therefore, the present study summarizes the lncRNAs role in PC and the probable strategies to deal with their expression and controlling tumorigenesis and detection of the prognosis of PC.

A Transformative Technology Linking Patient’s mRNA Expression Profile to Anticancer Drug Efficacy

As precision medicine such as targeted therapy and immunotherapy often have limited accessibility, low response rate, and evolved resistance, it is urgent to develop simple, low-cost, and quick-turnaround personalized diagnostic technologies for drug response prediction with high sensitivity, speed, and accuracy. The major challenges of drug response prediction strategies employing digital database modeling are the scarcity of labeled clinical data, applicability only to a few classes of drugs, and losing the resolution at the individual patient level. Although these challenges have been partially addressed by large-scale cancer cell line datasets and more patient-relevant cell-based systems, the integration of different data types and data translation from pre-clinical to clinical utilities are still far-fetched. To overcome the current limitations of precision medicine with a clinically proven drug response prediction assay, we have developed an innovative and proprietary technology based on in vitro patient testing and in silico data analytics. First, a patient-derived gene expression signature was established via the transcriptomic profiling of cell-free mRNA (cfmRNA) from the patient’s blood. Second, a gene-to-drug data fusion and overlaying mechanism to transfer data were performed. Finally, a semi-supervised method was used for the database searching, matching, annotation, and ranking of drug efficacies from a pool of ~700 approved, investigational, or clinical trial drug candidates. A personalized drug response report can be delivered to inform clinical decisions within a week. The PGA (patient-derived gene expression-informed anticancer drug efficacy) test has significantly improved patient outcomes when compared to the treatment plans without PGA support. The implementation of PGA, which combines patient-unique cfmRNA fingerprints with drug mapping power, has the potential to identify treatment options when patients are no longer responding to therapy and when standard-of-care is exhausted.

Enhanced hepatotoxicity assessment through encapsulated HepG2 spheroids in gelatin hydrogel matrices: Bridging the gap from 2D to 3D culture

Conventional 2D drug screening often fails to accurately predict clinical outcomes. We present an innovative approach to improve hepatotoxicity assessment by encapsulating HepG2 spheroids in gelatin hydrogel matrices with different mechanical properties. Encapsulated spheroids exhibit sustained liver-specific functionality, enhanced expression of drug-metabolizing enzymes, and increased drug sensitivity compared to 2D cultures. The platform detects critical variations in drug response, with significant differences in IC50 values between 2D and spheroid cultures ranging from 1.3-fold to > 13-fold, particularly for acetaminophen. Furthermore, drug-metabolizing enzyme expression varies across hydrogel concentrations, suggesting a role for matrix mechanical properties in modulating hepatocyte function. This novel spheroid-hydrogel platform offers a transformative approach to hepatotoxicity assessment, providing increased sensitivity, improved prediction, and a more physiologically relevant environment. The use of such advanced in vitro models can accelerate drug development, reduce animal testing, and contribute to improved patient safety and clinical outcomes.

Pharmacogenomics: A Genetic Approach to Drug Development and Therapy

The majority of the well-known pharmacogenomics research used in the medical sciences contributes to our understanding of medication interactions. It has a significant impact on treatment and drug development. The broad use of pharmacogenomics is required for the progress of therapy. The main focus is on how genes and an intricate gene system affect the body’s reaction to medications. Novel biomarkers that help identify a patient group that is more or less likely to respond to a certain medication have been discovered as a result of recent developments in the field of clinical therapeutics. It aims to improve customized therapy by giving the appropriate drug at the right dose at the right time and making sure that the right prescriptions are issued. A combination of genetic, environmental, and patient variables that impact the pharmacokinetics and/or pharmacodynamics of medications results in interindividual variance in drug response. Drug development, illness susceptibility, and treatment efficacy are all impacted by pharmacogenomics. The purpose of this work is to give a review that might serve as a foundation for the creation of new pharmacogenomics applications, techniques, or strategies.

Unveiling the Correlation Between the Membrane Assembly of P-gp and Drug Resistance in Multiple Myeloma Using Super-Resolution Fluorescence Imaging.

Drug resistance in multiple myeloma (MM) poses a significant challenge to treatment efficacy, primarily attributed to P-glycoprotein (P-gp) dysfunction. This study delves into the elusive spatial organization of P-gp, aiming to enhance our understanding of its role in MM drug resistance by exploring the intricate relationship between molecular function and spatial arrangement. Employing super-resolution imaging of P-gp with the inhibitor probe Tariquidar-TAMR labeling on MM cell membranes, the research uncovered a more pronounced clustering distribution of P-gp in drug-resistant cells (MM1R) compared to drug-sensitive counterparts (MM1S). Further exploration revealed the clustering distribution of P-gp was heightened as cellular drug resistance increased in hypoxic condition, directly emphasizing the strong correlation between P-gp cluster morphology and drug resistance. Additionally, stable P-gp cluster formation was influenced by cross-linking of membrane carbohydrates, and disrupting these glycoprotein clusters could reduce cellular drug resistance, suggesting that altering distribution patterns of P-gp can modulate drug responsiveness. Finally, dexamethasone (Dex) treatment was revealed to enhance P-gp clustering distribution, particularly in MM1S cells, indicating that change degree in P-gp distribution correlate with the modifiable space of cellular drug responsiveness. This study provides insights into the correlation between P-gp assembly and cellular drug responsiveness, deepening our understanding of functional changes in MM drug resistance and offering valuable perspectives for overcoming this challenge.

Isolation methods and characterization of primary rat neurovascular cells

BackgroundThere is significant interest in isolating cells of the blood-brain barrier (BBB) for use in in vitro screening of therapeutics and analyzing cell specific roles in neurovascular pathology. Primary brain cells play an advantageous role in BBB models; however, isolation procedures often do not produce cells at high enough yields for experiments. In addition, although numerous reports provide primary cell isolation methods, the field is lacking in documentation and detail of expected morphological changes that occur throughout culturing and there are minimal troubleshooting resources. Here, we present simplified, robust, and reproducible methodology for isolating astrocytes, pericytes, and endothelial cells, and demonstrate several morphological benchmarks for each cell type throughout the process and culture timeframe. We also analyze common considerations for developing neurovascular cell isolation procedures and recommend solutions for troubleshooting.ResultsThe presented methodology isolated astrocytes, pericytes, and endothelial cells and enabled cell attachment, maturation, and cell viability. We characterized milestones in cell maturation over 12 days in culture, a common timeline for applications of these cell types in BBB models. Phase contrast microscopy was used to show initial cell plating, attachment, and daily growth of isolated cells. Confocal microscopy images were analyzed to determine the identity of cell types and changes to cell morphology. Nuclear staining was also used to show the viability and proliferation of glial cells at four time points. Astrocyte branches became numerous and complex with increased culture time. Microglia, oligodendrocytes, and neurons were present in mixed glial cultures for 12 days, though the percentage of microglia and neurons expectedly decreased after passaging, with microglia demonstrating a less branched morphology.ConclusionsNeurovascular cells can be isolated through our optimized protocols that minimize cell loss and encourage the adhesion and proliferation of isolated cells. By identifying timepoints of viable glia and neurons within an astrocyte-dominant mixed culture, these cells can be used to evaluate drug targeting, uptake studies, and response to pathological stimulus in the neurovascular unit.

Ferroptosis-related alternative splicing signatures as potential biomarkers for predicting prognosis and therapy response in gastric cancer

Ferroptosis is linked to various tumor biological traits, and alternative splicing (AS), a crucial step in mRNA processing, plays a role in the post-transcriptional regulation of ferroptosis-related genes (FRGs). A least absolute shrinkage and selection operator (LASSO) penalized Cox regression analysis was utilized to build a prognostic signature based on 12 AS events (p < 0.05), which was validated in gastric cancer (GC) patients. The high-risk group (n = 203) showed enrichment in cancer and metastasis pathways (p < 0.05). Significant differences existed between the high- and low-risk groups in terms of tumor microenvironment (TME) cell infiltration and immune activities (p < 0.05). The low-risk group (n = 203) was characterized by immune activation and improved prognosis (p < 0.001). Additionally, targeted treatment and immunotherapy were more likely to benefit the low-risk group (p < 0.05). Correlation analysis was performed to detect related splicing factors (SF) (Cor>0.4, FDR<0.05). Furthermore, our functional assay results suggested that high SF3A2 expression might increase ferroptosis resistance and promote cell proliferation. In conclusion, the FRAs model we built has an advantage in predicting GC prognosis. The model's demonstration of variations in the immune microenvironment and drug response could potentially inform decisions regarding treatment strategies.

A clinically compatible in vitro drug-screening platform identifies therapeutic vulnerabilities in primary cultures of brain metastases.

Brain metastases represent the most common intracranial tumors in adults and are associated with a poor prognosis. We used a personalized in vitro drug screening approach to characterize individual therapeutic vulnerabilities in brain metastases. Short-term cultures of cancer cells isolated from brain metastasis patients were molecularly characterized using next-generation sequencing and functionally evaluated using high-throughput in vitro drug screening to characterize pharmacological treatment sensitivities. Next-generation sequencing identified matched genetic alterations in brain metastasis tissue samples and corresponding short-term cultures, suggesting that short-term cultures of brain metastases are suitable models for recapitulating the genetic profile of brain metastases that may determine their sensitivity to anti-cancer drugs. Employing a high-throughput in vitro drug screening platform, we successfully screened the cultures of five brain metastases for response to 267 anticancer compounds and related drug response to genetic data. Among others, we found that targeted treatment with JAK3, HER2, or FGFR3 inhibitors showed anti-cancer effects in individual brain metastasis cultures. Our preclinical study provides a proof-of-concept for combining molecular profiling with in vitro drug screening for predictive evaluation of therapeutic vulnerabilities in brain metastasis patients. This approach could advance the use of patient-derived cancer cells in clinical practice and might eventually facilitate decision-making for personalized drug treatment.

A Comprehensive Review: Pharmacogenomics and Personalized Medicine Customizing Drug Therapy Based on Individual Genetics Profiles.

Numerous factors, such as genetics, environmental factors, and illness determinants, might contribute to an unpleasant pharmaceutical response. In an effort to increase efficacy and safety, as well as to gain a better understanding of drug disposition and clinical consequences, researchers in the two quickly emerging fields of pharmacogenetics (which focuses on single genes) and pharmacogenomics (which focuses on many genes) have studied the genetic personalization of drug response. This is due to the fact that a large number of pharmacological responses seem to be genetically based, and the relationship between medication response and genotype may be important for diagnosis. We now have a better understanding of the genetic basis of individual medication responses because to research on pharmaceuticals and genes. Pharmacogenomics aims to improve patient outcomes by developing personalized medicine by using the diversity of the human genome and how it affects medication response. Translational in nature, pharmacogenomics research encompasses everything from the discovery of genotype-phenotype associations to clinical investigations that might show therapeutic relevance. Though the conversion of pharmacogenomics research findings into clinical practice has been sluggish, advances in the field offer considerable potential for future therapeutic applications in specific people.

A pan-cancer analysis unveiling the function of NR4A family genes in tumor immune microenvironment, prognosis, and drug response.

NR4A family genes play crucial roles in cancers. However, the role of NR4A family genes in cancers remains paradoxical as they promote or suppress tumorigenesis. We aimed to conduct comprehensive analyses of the association between the expression of NR4A family genes and tumor microenvironment (TME) based on bioinformatics methods. We collected RNA-seq data from 33 cancer types and 20 normal tissue sites from the TCGA and GTEx databases. Expression patterns of NR4A family genes and their associations with DNA methylation, miRNA, overall survival, drug responses, and tumor microenvironment were investigated. Significant downregulation of all NR4A family genes was observed in 15 cancer types. DNA promoter methylation and expression of NR4A family genes were negatively correlated in five cancers. The expression of 10 miRNAs targeting NR4A family genes was negatively correlated with the expression of NR4A family genes. High expression of all NR4A family genes was associated with poor prognosis in stomach adenocarcinoma and increased expressions of NR4A2 and NR4A3 were associated with poor prognosis in adrenocortical carcinoma. In addition, we found an elevated expression of NR4A2, which enhances the response to various chemotherapeutic drugs, whereas NR4A3 decreases drug sensitivity. Interestingly, in breast cancer, NR4A3 was significantly associated with C2 (IFN-γ dominant), C3 (inflammatory), and C6 (TGF-β dominant) immune subtypes and infiltrated immune cell types, implying both oncogenic and tumor-suppressive functions of NR4A3 in breast cancer. The NR4A family genes have the potential to serve as a diagnostic, prognostic, and immunological marker of human cancers.

An immune biomarker associated with EMT serves as a predictor for prognosis and drug response in bladder cancer.

Bladder cancer (BLCA), which develops from the upper endometrial of the bladder, is the sixth most prevalent cancer across the globe. WDHD1 (WD repeat and HMG-box DNA binding protein 1 gene) directly affects signaling, the cell cycle, and the development of the cell skeleton. Uncertainty surrounds WDHD1's function in BLCA immunity and prognosis, though. Using weighed gene co-expression network analysis (WGCNA), initially, we first identified 32 risk factors in genes with differential expression for this investigation. Then, using a variety of bioinformatic techniques and experimental validation, we examined the connections between WDHD1 and BLCA expression, clinical pathological traits, WDHD1-related proteins, upper-skin-intermediate conversion (EMT), immune cell immersion, convergence factors, immune markers, and drug sensitivity. The findings demonstrated that we constructed a 32-gene risk-predicting model where WDHD1 was elevated as a representative gene expression in BLCA and related to a range of clinical traits. Furthermore, high WDHD1 expression was a standalone predictor associated with a worse survival rate. The most commonly recruited cells and their evolutionary patterns were highlighted to better comprehend WDHD1's function in cancer. High WDHD1 expression was associated with many aspects of immunology. Finally, the study found that individuals with high expression of WDHD1 were drug-sensitive to four different broad-spectrum anti-cancer drugs. These results describe dynamic changes in the tumor microenvironment in BLCA and provide evidence for the hypothesis that WDHD1 is a novel biomarker of tumor development. WDHD1 may therefore be a useful target for the detection and management of BLCA.

A 3D Pancreatic Cancer Model with Integrated Optical Sensors for Noninvasive Metabolism Monitoring and Drug Screening.

A distinct feature of pancreatic ductal adenocarcinoma (PDAC) is a prominent tumor microenvironment (TME) with remarkable cellular and spatial heterogeneity that meaningfully impacts disease biology and treatment resistance. The dynamic crosstalk between cancer cells and the dense stromal compartment leads to spatially and temporally heterogeneous metabolic alterations, such as acidic pH that contributes to drug resistance in PDAC. Thus, monitoring the extracellular pH metabolic fluctuations within the TME is crucial to predict and to quantify anticancer drug efficacy. Here,a simple and reliable alginate-based 3D PDAC model embedding ratiometric optical pH sensors and cocultures of tumor (AsPC-1) and stromal cells for simultaneously monitoring metabolic pH variations and quantify drug response is presented. By means of time-lapse confocal laser scanning microscopy (CLSM) coupled with a fully automated computational analysis, the extracellular pH metabolic variations are monitored and quantified over time during drug testing with gemcitabine, folfirinox, and paclitaxel, commonly used in PDAC therapy. In particular, the extracellular acidification is more pronounced after drugs treatment, resulting in increased antitumor effect correlated with apoptotic cell death. These findings highlight the importance of studying the influence of cellular metabolic mechanisms on tumor response to therapy in 3D tumor models, this being crucial for the development of personalized medicine approaches.

RYR2 deficient human model identifies calcium handling and metabolic dysfunction impacting pharmacological responses

Creation of disease models utilizing hiPSCs in combination with CRISPR/Cas9 gene editing enable mechanistic insights into differential pharmacological responses. This allows translation of efficacy and safety findings from a healthy to a diseased state and provides a means to predict clinical outcome sooner during drug discovery. Calcium handling disturbances including reduced expression levels of the type 2 ryanodine receptor (RYR2) are linked to cardiac dysfunction; here we have created a RYR2 deficient human cardiomyocyte model that mimics some aspects of heart failure. RYR2 deficient cardiomyocytes show differential pharmacological responses to L-type channel calcium inhibitors. Phenotypic and proteomic characterization reveal novel molecular insights with altered expression of structural proteins including CSRP3, SLMAP, and metabolic changes including upregulation of the pentose phosphate pathway and increased sensitivity to redox alterations. This genetically engineered in vitro cardiovascular model of RYR2 deficiency supports the study of pharmacological responses in the context of calcium handling and metabolic dysfunction enabling translation of drug responses from healthy to perturbed cellular states.

A mechanistic analysis of metformin's biphasic effects on lifespan and healthspan in C. elegans: Elixir in youth, poison in elder

Aging, a complex biological process influenced by genetic, environmental, and pharmacological factors, presents a significant challenge in understanding its underlying mechanisms. In this study, we explored the divergent impacts of metformin treatment on the lifespan and healthspan of young and old C. elegans, demonstrating a intriguing “elixir in youth, poison in elder” phenomenon. By scrutinizing the gene expression changes in response to metformin in young (day 1 of adulthood) and old (days 8) groups, we identified nhr-57 and C46G7.1 as potential modulators of age-specific responses. Notably, nhr-57 and C46G7.1 exhibit contrasting regulation patterns, being up-regulated in young worms but down-regulated in old counterparts following metformin treatment. Functional studies employing knockdown approaches targeting nhr-57, a gene under the control of hif-1 with a documented protective function against pore-forming toxins in C. elegans, and C46G7.1, unveiled their critical roles in modulating lifespan and healthspan, as well as in mediating the biphasic effects of metformin. Furthermore, deletion of hif-1 retarded the influence of metformin, implicating the involvement of hif-1/nhr-57 in age-specific drug responses. These findings underscored the necessity of deciphering the mechanisms governing age-related susceptibility to pharmacological agents to tailor interventions for promoting successful aging.

RYR2 deficient human model identifies calcium handling and metabolic dysfunction impacting pharmacological responses

Creation of disease models utilizing hiPSCs in combination with CRISPR/Cas9 gene editing enable mechanistic insights into differential pharmacological responses. This allows translation of efficacy and safety findings from a healthy to a diseased state and provides a means to predict clinical outcome sooner during drug discovery. Calcium handling disturbances including reduced expression levels of the type 2 ryanodine receptor (RYR2) are linked to cardiac dysfunction; here we have created a RYR2 deficient human cardiomyocyte model that mimics some aspects of heart failure. RYR2 deficient cardiomyocytes show differential pharmacological responses to L-type channel calcium inhibitors. Phenotypic and proteomic characterization reveal novel molecular insights with altered expression of structural proteins including CSRP3, SLMAP, and metabolic changes including upregulation of the pentose phosphate pathway and increased sensitivity to redox alterations. This genetically engineered in vitro cardiovascular model of RYR2 deficiency supports the study of pharmacological responses in the context of calcium handling and metabolic dysfunction enabling translation of drug responses from healthy to perturbed cellular states.